In the field of medical imaging, medical images are often required to be aligned for comparison. For example, a current image of a patient may be aligned with a prior image of the same patient to assess disease progression or results of treatment.
It is known to compare or combine images that have been obtained using different modalities of imaging, for example X-ray computed tomography (CT) and magnetic resonance imaging (MR), to take advantage of the different information that is obtained from scans in each modality. Different modalities may provide different levels of contrast detection for different tissue types. Some regions, tissues or anatomical features may show up more on CT than on MR or vice versa. CT scans may be better for anatomical information while MR scans may be better for softer tissues and for functional information.
However, in general, the position of anatomical features will differ between different images, for example due to different patient positioning, patient movement, different modalities of imaging or different imaging parameters. Therefore images must be aligned for comparison. Alignment may refer to any way of mapping two image data sets together, including manual alignment, mechanical alignment or alignment by registration, for example by using registration software.
In order to achieve accurate alignment of two images of a given anatomical feature, it is necessary to transform one of the images so that the coordinates of the anatomical feature are the same in each resulting image. This is achieved by the process of image registration and transformation.
Rigid registration refers to a class of techniques for aligning two or more images or volumes by way of rigid transformations (transformations that involve only rotation and translation parameters). Affine registration is a registration using affine transformations (rotation, translation, scaling, or shearing).
For image registration, it is sometimes useful to use rotation, translation, and a uniform scaling component. This gives a space of transformations that is more general than rigid transformations, but not as general as affine transformations.
Non-rigid registration refers to a class of techniques that use more general transformations that allow for deformation, including local deformation.
Techniques for registration of images are well-known. In general, registration is an optimization problem, with the aim of finding an optimal transform between two images, which relates corresponding features in the images by mapping points in the coordinate system of one image onto the corresponding points in the coordinate system of the other image.
A similarity measure is a measure of the similarity between two images. For example, in the mutual information (MI) approach, points in each image are identified and correlated by the statistical similarity (mutual information) between the two images.
For registration of two images, one image may be kept constant and the other transformed according to a set of parameters defined by the type of registration (for example, in rigid registration, rotation and translation parameters in the appropriate number of dimensions). The similarity measure between the two resulting images is then determined. This defines an objective function from the parameters to the similarity measure. The objective function is then optimized using an optimization function, for example, gradient descent, hill climbing or Powell optimization, to achieve an optimal transform relating the two images. This optimal transform is applied to the second image to obtain a transformed image that is aligned with the first image, and has a common coordinate system.
Registration can be performed manually (for example, by manual selection of corresponding points on each image), semi-automatically, or automatically. Many medical imaging systems now have greater automation of registration than was previously the case.
Results of automatic registration may be evaluated by comparison to validation data (which may also be called ground truth), for example by comparison to the results of manual registration that has been performed by a clinical expert.
It is of interest to register tubular structures in the body, for example blood vessels. One motivation for registering blood vessels is to compare images of blood vessels that were taken at different times to assess the progress of diseased vessels, for example those having stenosis or aneurysm. In such comparisons, it is important that the vessel is registered correctly so that the vessels may be accurately compared, but it is also important that the registration process does not remove changes in the vessel that may result from disease.
An abdominal aortic aneurysm (AAA) is a ballooning of the aorta due to weakness in the vessel wall. In patients having an AAA, there is a risk of aneurysm rupture, which is fatal in 65% to 85% of cases (Kniemeyer et al. Eur J Vasc Endovasc Surg 2000; 19:190-196). In 2000 there were approximately 6800 deaths from AAA rupture in England and Wales alone (Vardulaki et al. Br J Surg 2000; 87:195-200). In men over 65, AAA rupture is responsible for 2.1% of all deaths (Wilmink et al. J Vasc Surg 1999; 30:203-8). AAAs are usually completely asymptomatic prior to rupture. Many AAAs may be detected coincidentally when a medical image of the patient is taken for another medical purpose.
Ultrasound examination may be the modality of choice for screening for AAAs. Under current medical practices, if a patient is found to have an MA, the patient may undergo ultrasound surveillance at periodic intervals, for example at 6 month, 12 month or 24 month intervals. Each ultrasound surveillance scan may result in a single value for the diameter of the aneurysm. It may be difficult to measure the same aneurysm diameter on each scan. For example the diameter may be measured at different angles on different scans.
At present, surgical review may be based on the size of the aneurysm. For example, only patients having an aneurysm diameter of 5.5 cm or greater may be scheduled for surgery. However, it has been found that the size of the aneurysm may not necessarily be a good indicator of the likelihood of aneurysm rupture. Some aneurysms may rupture when below 5.5 cm in diameter. Some aneurysms may reach 10 to 12 cm in diameter without rupturing.
In patients known to have an aneurysm, more invasive follow-up may be justified to determine those patients who are most at risk of rupture. Acquiring more detailed imaging data for such patients may improve knowledge of factors that may cause the aneurysm to rupture. For example, it may be possible to analyze the composition of the aneurysm.
Such follow-up may include studies using imaging modalities such as MR and CT, or other modalities. Multimodality scans may be performed to get different information from each modality. For example, when imaging an AAA, as the scan moves through the thrombus, some areas may show up more on CT, while others show up more on MR.
Scans of different modalities may be taken at the same time, for example on the same day. Alternatively, images of the same or different modality may be taken as part of a longitudinal study, where images are taken over a period of time, for example images are taken on different days, weeks or months. Longitudinal follow-up aims to detect changes in the form of the aneurysm with time. Longitudinal follow-up by MR or CT is likely to benefit from accurate image registration.
Standard clinical care of patients whose aneurysms are being monitored may require either MR or CT alone. However, one current scanning protocol is to take two MRI scans a day apart, the second of which has a contrast agent that targets inflammation, and also to take a CT scan first day. Imaging that uses USPIO (ultrasmall superparamagnetic iron oxide) contract agents may be performed in this manner.
The abdomen is non-rigid. Different organs and structures within the abdomen move independently of each other. A single rigid registration cannot therefore correct accurately for motion in the abdomen.
However, using a non-rigid registration algorithm on the abdomen (including the abdominal aorta) without any further constraints may mask genuine changes in an AAA. The non-rigid registration will match the form of the aneurysm between the images being registered and thus a change in the aneurysm may no longer be distinguishable after registration.
Similar considerations may also apply to aneurysms occurring in other parts of the body, for example in the heart, or to other medical conditions, for example stenosis, that may occur in tubular structures such as the arteries.
One method that has been proposed for registering the aorta proposes to register two computed tomography angiography (CTA) images of an aorta by first segmenting the aorta from each image and then registering the two objects that result from the segmentation using manual registration.